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Iterative Blind Deconvolution Algorithm for Deblurring a Single PSP/TSP Image of Rotating Surfaces.

Anshuman Pandey1, James W Gregory2

  • 1Aerospace Research Center, The Ohio State University, 2300 West Case Road, Columbus, OH 43235, USA. pandey.46@osu.edu.

Sensors (Basel, Switzerland)
|September 16, 2018
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Summary
This summary is machine-generated.

This study presents an iterative image deconvolution method to improve pressure-sensitive paint (PSP) imaging on moving surfaces. The new technique uses a spatially variant point spread function (PSF) to accurately correct for motion blur in PSP measurements.

Keywords:
deblurringpolymer/ceramic (PC-PSP)pressure-sensitive paint (PSP)rotating surface pressure measurementtemperature-sensitive paint (TSP)

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Area of Science:

  • Fluid dynamics
  • Optical measurement techniques
  • Image processing

Background:

  • Imaging pressure-sensitive paint (PSP) on moving surfaces suffers from motion blur due to finite exposure times.
  • High-sensitivity PSP with long luminescence decay times exacerbate image blurring, particularly at blade edges.
  • Current deconvolution methods often assume a spatially invariant time constant, leading to inaccuracies with strong pressure gradients.

Purpose of the Study:

  • To develop an iterative image deconvolution method for accurate PSP imaging on moving surfaces.
  • To address the limitations of spatially invariant point spread functions (PSFs) in PSP deblurring.
  • To implement a spatially variant PSF that accounts for local pressure variations.

Main Methods:

  • Introduced an iterative deconvolution scheme employing a spatially variant PSF.
  • Estimated point-by-point PSF values iteratively, as the time constant depends on local pressure.
  • Utilized a kernel-free forward model for generating test images and a spinning disk setup for real-world validation.

Main Results:

  • The iterative method successfully estimated a super-resolved, spatially varying blur kernel with sub-pixel resolution.
  • Restored images using classical iterative regularization tools showed improved accuracy.
  • Demonstrated applicability across various experimental conditions, including strong pressure gradients.

Conclusions:

  • The proposed iterative deconvolution with a spatially variant PSF effectively reduces motion blur in PSP imaging.
  • This method overcomes the inaccuracies of previous spatially invariant approaches, especially in areas with significant pressure changes.
  • The technique offers enhanced accuracy for pressure measurements on dynamic surfaces.